Systems and methods for determining navigation patterns associated with a social networking system

ABSTRACT

Systems, methods, and non-transitory computer readable media can obtain user navigation data associated with transitions by users between one or more pages associated with a system. Reduced dimensionality user navigation data can be generated based on the user navigation data. A plurality of clusters can be generated based on the reduced dimensionality user navigation data, wherein each cluster of the plurality of clusters corresponds to a user navigation pattern associated with the system.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No. 15/604,310, filed on May 24, 2017 and entitled “SYSTEMS AND METHODS FOR DETERMINING NAVIGATION PATTERNS ASSOCIATED WITH A SOCIAL NETWORKING SYSTEM”, which is incorporated in its entirety herein by reference.

FIELD OF THE INVENTION

The present technology relates to the field of social networks. More particularly, the present technology relates to techniques for determining user navigation behavior associated with social networking systems.

BACKGROUND

Today, people often utilize computing devices (or systems) for a wide variety of purposes. Users can use their computing devices, for example, to interact with one another, create content, share content, and view content. In some cases, a user can utilize his or her computing device to access a social networking system (or service). The user can provide, post, share, and access various content items, such as status updates, images, videos, articles, and links, via the social networking system.

A social networking system may provide resources through which users may publish content items. In one example, a content item can be presented on a profile page of a user. As another example, a content item can be presented through a feed for a user.

SUMMARY

Various embodiments of the present disclosure can include systems, methods, and non-transitory computer readable media configured to obtain user navigation data associated with transitions by users between one or more pages associated with a system. Reduced dimensionality user navigation data can be generated based on the user navigation data. A plurality of clusters can be generated based on the reduced dimensionality user navigation data, wherein each cluster of the plurality of clusters corresponds to a user navigation pattern associated with the system.

In some embodiments, a transition includes a navigation from a first page of the one or more pages to a second page of the one or more pages.

In certain embodiments, a number of possible types of transitions included in the transitions is equal to a difference between a square of a number of the one or more pages and the number of the one or more pages.

In an embodiment, the obtaining the user navigation data comprises generating a frequency vector for each of the users, wherein the frequency vector includes a frequency associated with each type of transition of the possible types of transitions.

In some embodiments, the generating the reduced dimensionality user navigation data comprises determining a singular value decomposition (SVD) of the frequency vectors for the users.

In certain embodiments, the singular value decomposition of the frequency vectors for the users includes a matrix of reduced user vectors, a matrix of eigenvectors, and a matrix of eigenvalues.

In an embodiment, the generating the plurality of clusters comprises clustering the matrix of reduced user vectors.

In some embodiments, the generating the plurality of clusters is based on k-means clustering.

In certain embodiments, the user navigation pattern indicated by each cluster of the plurality of clusters is characterized by a centroid of the cluster.

In an embodiment, one or more of features and content associated with the system can be customized based on the plurality of clusters.

It should be appreciated that many other features, applications, embodiments, and/or variations of the disclosed technology will be apparent from the accompanying drawings and from the following detailed description. Additional and/or alternative implementations of the structures, systems, non-transitory computer readable media, and methods described herein can be employed without departing from the principles of the disclosed technology.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example system including an example user navigation pattern module configured to determine user navigation patterns associated with a system, according to an embodiment of the present disclosure.

FIG. 2A illustrates an example user navigation data module configured to determine user navigation data, according to an embodiment of the present disclosure.

FIG. 2B illustrates an example reduced dimensionality data module configured to generate reduced dimensionality user navigation data, according to an embodiment of the present disclosure.

FIG. 3 illustrates an example scenario for determining user navigation patterns associated with a system, according to an embodiment of the present disclosure.

FIG. 4 illustrates an example first method for determining user navigation patterns associated with a system, according to an embodiment of the present disclosure.

FIG. 5 illustrates an example second method for determining user navigation patterns associated with a system, according to an embodiment of the present disclosure.

FIG. 6 illustrates a network diagram of an example system that can be utilized in various scenarios, according to an embodiment of the present disclosure.

FIG. 7 illustrates an example of a computer system that can be utilized in various scenarios, according to an embodiment of the present disclosure.

The figures depict various embodiments of the disclosed technology for purposes of illustration only, wherein the figures use like reference numerals to identify like elements. One skilled in the art will readily recognize from the following discussion that alternative embodiments of the structures and methods illustrated in the figures can be employed without departing from the principles of the disclosed technology described herein.

DETAILED DESCRIPTION

Determining Navigation Patterns Associated with a Social Networking System

People use computing devices (or systems) for a wide variety of purposes. Computing devices can provide different kinds of functionality. Users can utilize their computing devices to produce information, access information, and share information. In some cases, users can utilize computing devices to interact or engage with a conventional social networking system (e.g., a social networking service, a social network, etc.). A social networking system may provide resources through which users may publish content items. In one example, a content item can be presented on a profile page of a user. As another example, a content item can be presented through a feed for a user.

Different users of a social networking system can have varying usage patterns. For example, users having a particular demographic attribute can be more interested in consuming video content than other users. Conventional approaches specifically arising in the realm of computer technology may group users based on various demographic attributes, such as age, age range, gender, etc. For example, conventional approaches may group users in order to analyze data associated with users and provide customized content or experience to different groups of users. However, grouping users based on demographic attributes may not take into account similarity of navigation patterns among users.

An improved approach rooted in computer technology can overcome the foregoing and other disadvantages associated with conventional approaches specifically arising in the realm of computer technology. Based on computer technology, the disclosed technology can determine navigation patterns associated with users of a social networking system. Content or data of a social networking system can be presented as one or more pages in a user interface. For example, pages associated with the social networking system can include a feed page, a user profile page, a notifications page, and a content item page. A user can navigate between different pages in the user interface. The disclosed technology can monitor transitions by users between pages in order to determine navigation patterns associated with the users. A frequency vector for a user can indicate frequencies of transitions between different pages for the user. Frequency vectors for users can be reduced or decomposed in order to generate reduced dimensionality data. In some embodiments, the reduced dimensionality data can be generated based on singular value decomposition (SVD) or principal component analysis (PCA). The reduced dimensionality data can include reduced or decomposed user vectors. Reduced user vectors can be clustered in order to determine groups of users having similar navigation patterns associated with the social networking system. Information relating to groups of users based on navigation patterns can be used in various ways, for example, to customize features and/or content presented on different pages. Details relating to the disclosed technology are provided below.

FIG. 1 illustrates an example system 100 including an example user navigation pattern module 102 configured to determine user navigation patterns associated with a system, according to an embodiment of the present disclosure. The user navigation pattern module 102 can include a user navigation data module 104 and a reduced dimensionality data module 106. In some instances, the example system 100 can include at least one data store 120. The components (e.g., modules, elements, steps, blocks, etc.) shown in this figure and all figures herein are exemplary only, and other implementations may include additional, fewer, integrated, or different components. Some components may not be shown so as not to obscure relevant details. In various embodiments, one or more of the functionalities described in connection with the user navigation pattern module 102 can be implemented in any suitable combinations. While the disclosed technology is described in connection with user navigation between pages associated with a social networking system for illustrative purposes, the disclosed technology can apply to any other type of system and/or data in any format.

The user navigation data module 104 can determine user navigation data. The user navigation data module 104 can obtain data associated with transitions between pages of an application for users and generate frequency vectors for users. Functionality of the user navigation data module 104 is described in more detail herein.

The reduced dimensionality data module 106 can generate reduced dimensionality user navigation data. For example, frequency vectors for users can be reduced and/or decomposed. Users can be clustered based on the reduced dimensionality user navigation data. Functionality of the reduced dimensionality data module 106 is described in more detail herein.

In some embodiments, the user navigation pattern module 102 can be implemented, in part or in whole, as software, hardware, or any combination thereof. In general, a module as discussed herein can be associated with software, hardware, or any combination thereof. In some implementations, one or more functions, tasks, and/or operations of modules can be carried out or performed by software routines, software processes, hardware, and/or any combination thereof. In some cases, the user navigation pattern module 102 can be, in part or in whole, implemented as software running on one or more computing devices or systems, such as on a server system or a client computing device. In some instances, the user navigation pattern module 102 can be, in part or in whole, implemented within or configured to operate in conjunction or be integrated with a social networking system (or service), such as a social networking system 630 of FIG. 6. Likewise, in some instances, the user navigation pattern module 102 can be, in part or in whole, implemented within or configured to operate in conjunction or be integrated with a client computing device, such as the user device 610 of FIG. 6. For example, the user navigation pattern module 102 can be implemented as or within a dedicated application (e.g., app), a program, or an applet running on a user computing device or client computing system. It should be understood that many variations are possible.

The data store 120 can be configured to store and maintain various types of data, such as the data relating to support and operation of the user navigation pattern module 102. The data maintained by the data store 120 can include, for example, information relating to user navigation data, frequency vectors, reduced dimensionality data, clustering data, navigation patterns, user groups based on navigation patterns, etc. The data store 120 also can maintain other information associated with a social networking system. The information associated with the social networking system can include data about users, social connections, social interactions, locations, geo-fenced areas, maps, places, events, groups, posts, communications, content, account settings, privacy settings, and a social graph. The social graph can reflect all entities of the social networking system and their interactions. As shown in the example system 100, the user navigation pattern module 102 can be configured to communicate and/or operate with the data store 120. In some embodiments, the data store 120 can be a data store within a client computing device. In some embodiments, the data store 120 can be a data store of a server system in communication with the client computing device.

FIG. 2A illustrates an example user navigation data module 202 configured to determine user navigation data, according to an embodiment of the present disclosure. In some embodiments, the user navigation data module 104 of FIG. 1 can be implemented with the example user navigation data module 202. As shown in the example of FIG. 2A, the example user navigation data module 202 can include a transition data module 204 and a frequency vector module 206.

The transition data module 204 can obtain data relating to transitions by users between pages associated with a social networking system. Content or data associated with the social networking system can be organized or presented in a user interface as one or more pages. In some embodiments, a user can access content or data associated with the social networking system via an application (“app”), and the application can present content in one or more pages. For example, an application, such as a social networking application, can include the following pages: a feed page, a notifications page, a profile page, a content item page, a videos page, a photos page, a web view page, a feedback page, etc. A feed page can provide one or more content items created by users. A notifications page can provide notifications relating to various actions or activities associated with the application. A profile page can provide information associated with a user. A content item page can display details of a specific content item. A videos page can provide one or more videos. A photos page can provide one or more photos. A web view page can provide a view of content as presented in a web browser. A feedback page can allow users to provide feedback associated with the application. A page of an application can be displayed in a user interface. A user can navigate between different pages of the application in the user interface. For instance, the user can navigate from one page of the application to another page of the application, for example, by selecting a menu item or by performing a gesture (e.g., a touch gesture). Examples of menu items can include buttons, tabs, icons, etc. A transition can indicate a transition or navigation from a first page of the application to a second page of the application. If the application includes n pages, there can be n²−n possible types of transitions. In some embodiments, a state of not using an application (“off application state”) can be treated as a page such that a transition to and from the off application state can be considered in user navigation patterns. Many variations are possible.

The transition data module 204 can monitor transitions of users. For example, the transition data module 204 can maintain counts for different types of transitions for each user. A count of a type of transition can be referred to as a transition count. The transition data module 204 can monitor transitions for any selected time period (e.g., minute(s), day(s), month(s), year(s), etc.). In some embodiments, transitions for users can be maintained in a table. As an example, the table can include a user identifier (ID), an origin page, a destination page, and a number of counts for a transition from an origin page and a destination page. For instance, the table can include the following information:

TABLE 1 user ID origin page destination page count User 1 Page 1 Page 2 m User 1 Page 1 Page 3 n In Table 1, user ID can indicate an identifier for a user, origin page can indicate a page from which the user navigates, destination page can indicate a page to which the user navigates, and count can indicate a count of a number of transitions from an origin page to a destination page. In Table 1, two different types of transitions are associated with User 1: a transition from Page 1 to Page 2 and a transition from Page 1 to Page 3. Respective counts associated with the two types of transitions are m and n.

The transition data module 204 can normalize transition counts for users. For example, transition counts for different types of transitions for each user can be normalized to add up to 1. In some embodiments, normalized transition counts can be maintained in a table. For example, the table can include a user ID, an origin page, a destination page, and a frequency of a transition from an origin page to a destination page. The frequency of a transition from an origin page to a destination page can indicate a frequency of a particular type of transition (e.g., a transition from a particular origin page to a particular destination page). The frequency be determined as a fraction of a count of a transition from a particular origin page and a particular destination page over a count of all types of transitions. For example, the frequency can be determined according to the following equation:

$\begin{matrix} {{{frequency} = \frac{count}{\sum\limits_{i\rightarrow j}{count}}},} & (1) \end{matrix}$

where frequency indicates a frequency of a particular type of transition, count indicates a count for the particular type of transition, i and j each indicate a page, i→j indicates a transition from i to j, and Σ_(i→j) count indicates a sum of counts for all possible types of transitions (e.g., all possible transitions i→j). In some embodiments, i→j can exclude cases where i and j indicate the same page. Accordingly, in an example, the table can include the following information:

TABLE 2 user ID origin page destination page Frequency User 1 Page 1 Page 2 m/(m + n) User 1 Page 1 Page 3  n/(m + n) In Table 2, user ID can indicate an identifier for a user, origin page can indicate a page from which the user navigates, destination page can indicate a page to which the user navigates, and frequency can indicate a frequency of a transition from an origin page to a destination page. In Table 2, there are two types of transitions for User 1: a first transition type from Page 1 to Page 2 and a second transition type from Page 1 to Page 3. In this regard, m can indicate a count of first transitions, while n can indicate a count of second transitions. Frequencies associated with the first type of transition and the second type of transition are, respectively, m/(m+n) and n/(m+n).

The frequency vector module 206 can generate a frequency vector for each user indicating frequencies of different types of transitions. A frequency vector for a user can include all possible types of transitions and indicate corresponding frequencies for all possible types of transitions. If a user does not have a count for a particular type of transition, the frequency for that type of transition can be 0. In some embodiments, the frequencies for all possible types of transitions for a user can add up to 1. As an example, a frequency vector for a user can be generated in the following format:

-   -   {i₁→j₁: 0.1, i₁→j₂:0.03, . . . , i_(n)→j_(n-1): 0.0,         i_(n)→j_(n): 0.15},         where i_(n) and j_(n) each indicate a page, i_(n)→j_(n)         indicates a particular type of transition, and each value (or         number) indicates a frequency for a particular type of         transition. In some embodiments, i_(n) and j_(n) are different         pages. All examples herein are provided for illustrative         purposes, and there can be many variations and other         possibilities.

FIG. 2B illustrates an example reduced dimensionality data module 252 configured to generate reduced dimensionality user navigation data, according to an embodiment of the present disclosure. In some embodiments, the reduced dimensionality data module 106 of FIG. 1 can be implemented with the example reduced dimensionality data module 252. As shown in the example of FIG. 2B, the example reduced dimensionality data module 252 can include a reduced dimensionality data generation module 254 and a clustering module 256.

The reduced dimensionality data generation module 254 can generate reduced dimensionality data based on user navigation data, for example, from the user navigation data module 202. For example, frequency vectors for users can be reduced in dimensionality and/or decomposed in order to simplify the frequency vectors. Frequency vectors can be reduced and/or decomposed based on various techniques, such as singular value decomposition (SVD) and principal component analysis (PCA). In some embodiments, the reduced dimensionality data generation module 254 can generate reduced dimensionality data based on SVD. As an example, SVD can be determined according to the following equation:

M=UΣV*  (2),

where M indicates a m×n real or complex matrix, U indicates a m×m real or complex unitary matrix, Σ indicates a m×n rectangular diagonal matrix with non-negative real numbers on the diagonal, V indicates an n×n real or complex unitary matrix, and V* indicates a conjugate transpose of V. UΣV* can be referred to as a singular value decomposition of M.

In an example applying SVD to frequency vectors, M can indicate a matrix of frequency vectors, and UΣV* can indicate a singular value decomposition of the matrix of frequency vectors. U can indicate a matrix of reduced or decomposed user vectors having a size of number of users by a number of dimensions. A number of users can refer to a number of users whose frequency vectors are included in the matrix of frequency vectors, and a number of dimensions can refer to a number of dimensions selected from possible types of transitions (e.g., n²−n). V* can indicate a matrix of eigenvectors that indicate directions of variants for frequency vectors in a Euclidean space. Σ can indicate a matrix of eigenvalues that represent magnitudes of eigenvectors. Eigenvectors and corresponding eigenvalues can provide information relating to various types of transitions. For example, a first eigenvector (e.g., an eigenvector having a largest eigenvalue) can describe most variants for frequency vectors, a second eigenvector (e.g., an eigenvector having a second largest eigenvalue) can describe second most variants for frequency vectors, and so forth. Each eigenvector can include weights associated with one or more types of transitions. The first eigenvector can indicate a direction in the Euclidean space that accounts for most frequency vectors and can provide information associated with most common navigation characteristics. The second eigenvector can indicate a direction in the Euclidean space that accounts for second most frequency vectors and can provide information associated with second most common navigation characteristics. The reduced user vectors (e.g., as included in U) can indicate individual differences for users after navigation characteristics associated with the eigenvectors have been removed.

The clustering module 256 can cluster users based on the reduced dimensionality data. As an example, clustering can be performed on reduced user vectors generated by the reduced dimensionality data generation module 254. In some embodiments, users can be clustered based on k-means clustering techniques. A number of clusters can be selected as appropriate. A number of dimensions used in determining clusters can be selected as appropriate. For example, the number of dimensions can be selected from possible types of transitions (e.g., n²−n). Each cluster can be associated with a particular user navigation pattern. For example, users in a first cluster may primarily navigate from a notifications page of an application to another page of the application. For the users in the first cluster, the notifications page can be a main starting point for accessing content. As another example, users in a second cluster may primarily navigate from a feed page to a content item page. For the users in the second cluster, the feed page can be a main starting point for accessing content. A user navigation pattern associated with a cluster can be characterized by a centroid of the cluster. For example, weights associated with various types of transitions for the centroid can be multiplied by eigenvectors and eigenvalues in order to obtain a description or a profile of the cluster. Content or features can be customized for users based on associated user navigation pattern clusters. For example, for the users in the first cluster who navigate from the notifications page, more content can be provided in the notifications page. In some embodiments, consistency of clusters over time can be determined by obtaining user navigation data and determining clusters periodically. All examples herein are provided for illustrative purposes, and there can be many variations and other possibilities.

FIG. 3 illustrates an example scenario 300 for determining user navigation patterns associated with a system, according to an embodiment of the present disclosure. In the example scenario 300, a frequency vector matrix 310 for users can be determined. For example, the frequency vector matrix 310 can include frequency vectors for various users. A frequency vector for a user can include frequencies for all types of transitions for the user. The frequency vectors can be determined based on user navigation data, which can indicate transitions between pages of an application for users. Dimensionality reduction can be performed on the frequency vector matrix 310 in order to generate reduced dimensionality user navigation data. For example, singular value decomposition can be performed on the frequency vector matrix 310. The singular value decomposition 320 of the frequency vector matrix 310 can include a reduced user vector matrix 321, an eigenvalue matrix 322, and an eigenvector matrix 323. The reduced user vector matrix 321, the eigenvalue matrix 322, and the eigenvector matrix 323 correspond to U, Σ, and V* of the singular value decomposition 320, respectively. If the frequency vector matrix 310 has a size of m×n, the reduced user vector matrix 321 can have a size of m×m, the eigenvalue matrix 322 can have a size of m×n, and the eigenvector matrix 323 can have a size of n×n. One or more clusters 330 can be generated based on the singular value decomposition 320 of the frequency vector matrix 310. For example, the reduced user vector matrix 321 can be clustered.

FIG. 4 illustrates an example first method 400 for determining user navigation patterns associated with a system, according to an embodiment of the present disclosure. It should be understood that there can be additional, fewer, or alternative steps performed in similar or alternative orders, or in parallel, based on the various features and embodiments discussed herein unless otherwise stated.

At block 402, the example method 400 can obtain user navigation data associated with transitions by users between one or more pages associated with a system. At block 404, the example method 400 can generate reduced dimensionality user navigation data based on the user navigation data. At block 406, the example method 400 can generate a plurality of clusters based on the reduced dimensionality user navigation data, wherein each cluster of the plurality of clusters corresponds to a user navigation pattern associated with the system. Other suitable techniques that incorporate various features and embodiments of the present disclosure are possible.

FIG. 5 illustrates an example second method 500 for determining user navigation patterns associated with a system, according to an embodiment of the present disclosure. It should be understood that there can be additional, fewer, or alternative steps performed in similar or alternative orders, or in parallel, based on the various features and embodiments discussed herein unless otherwise stated. Certain steps of the method 500 may be performed in combination with the example method 400 explained above.

At block 502, the example method 500 can generate a frequency vector for each of users. The frequency vector can include a frequency associated with each type of transition of the possible types of transitions. The transitions can be similar to the transitions explained in connection with FIG. 4. At block 504, the example method 500 can determine a singular value decomposition (SVD) of the frequency vectors for the users. At block 506, the example method 500 can generate a plurality of clusters based on a matrix of reduced user vectors. For example, the singular value decomposition of the frequency vectors for the users can include the matrix of reduced user vectors, a matrix of eigenvectors, and a matrix of eigenvalues. The plurality of clusters can be similar to the plurality of clusters explained in connection with FIG. 4. Other suitable techniques that incorporate various features and embodiments of the present disclosure are possible.

It is contemplated that there can be many other uses, applications, features, possibilities, and/or variations associated with various embodiments of the present disclosure. For example, users can, in some cases, choose whether or not to opt-in to utilize the disclosed technology. The disclosed technology can, for instance, also ensure that various privacy settings, preferences, and configurations are maintained and can prevent private information from being divulged. In another example, various embodiments of the present disclosure can learn, improve, and/or be refined over time.

Social Networking System—Example Implementation

FIG. 6 illustrates a network diagram of an example system 600 that can be utilized in various scenarios, in accordance with an embodiment of the present disclosure. The system 600 includes one or more user devices 610, one or more external systems 620, a social networking system (or service) 630, and a network 650. In an embodiment, the social networking service, provider, and/or system discussed in connection with the embodiments described above may be implemented as the social networking system 630. For purposes of illustration, the embodiment of the system 600, shown by FIG. 6, includes a single external system 620 and a single user device 610. However, in other embodiments, the system 600 may include more user devices 610 and/or more external systems 620. In certain embodiments, the social networking system 630 is operated by a social network provider, whereas the external systems 620 are separate from the social networking system 630 in that they may be operated by different entities. In various embodiments, however, the social networking system 630 and the external systems 620 operate in conjunction to provide social networking services to users (or members) of the social networking system 630. In this sense, the social networking system 630 provides a platform or backbone, which other systems, such as external systems 620, may use to provide social networking services and functionalities to users across the Internet.

The user device 610 comprises one or more computing devices that can receive input from a user and transmit and receive data via the network 650. In one embodiment, the user device 610 is a conventional computer system executing, for example, a Microsoft Windows compatible operating system (OS), Apple OS X, and/or a Linux distribution. In another embodiment, the user device 610 can be a device having computer functionality, such as a smart-phone, a tablet, a personal digital assistant (PDA), a mobile telephone, etc. The user device 610 is configured to communicate via the network 650. The user device 610 can execute an application, for example, a browser application that allows a user of the user device 610 to interact with the social networking system 630. In another embodiment, the user device 610 interacts with the social networking system 630 through an application programming interface (API) provided by the native operating system of the user device 610, such as iOS and ANDROID. The user device 610 is configured to communicate with the external system 620 and the social networking system 630 via the network 650, which may comprise any combination of local area and/or wide area networks, using wired and/or wireless communication systems.

In one embodiment, the network 650 uses standard communications technologies and protocols. Thus, the network 650 can include links using technologies such as Ethernet, 802.11, worldwide interoperability for microwave access (WiMAX), 3G, 4G, CDMA, GSM, LTE, digital subscriber line (DSL), etc. Similarly, the networking protocols used on the network 650 can include multiprotocol label switching (MPLS), transmission control protocol/Internet protocol (TCP/IP), User Datagram Protocol (UDP), hypertext transport protocol (HTTP), simple mail transfer protocol (SMTP), file transfer protocol (FTP), and the like. The data exchanged over the network 650 can be represented using technologies and/or formats including hypertext markup language (HTML) and extensible markup language (XML). In addition, all or some links can be encrypted using conventional encryption technologies such as secure sockets layer (SSL), transport layer security (TLS), and Internet Protocol security (IPsec).

In one embodiment, the user device 610 may display content from the external system 620 and/or from the social networking system 630 by processing a markup language document 614 received from the external system 620 and from the social networking system 630 using a browser application 612. The markup language document 614 identifies content and one or more instructions describing formatting or presentation of the content. By executing the instructions included in the markup language document 614, the browser application 612 displays the identified content using the format or presentation described by the markup language document 614. For example, the markup language document 614 includes instructions for generating and displaying a web page having multiple frames that include text and/or image data retrieved from the external system 620 and the social networking system 630. In various embodiments, the markup language document 614 comprises a data file including extensible markup language (XML) data, extensible hypertext markup language (XHTML) data, or other markup language data. Additionally, the markup language document 614 may include JavaScript Object Notation (JSON) data, JSON with padding (JSONP), and JavaScript data to facilitate data-interchange between the external system 620 and the user device 610. The browser application 612 on the user device 610 may use a JavaScript compiler to decode the markup language document 614.

The markup language document 614 may also include, or link to, applications or application frameworks such as FLASH™ or Unity™ applications, the SilverLight™ application framework, etc.

In one embodiment, the user device 610 also includes one or more cookies 616 including data indicating whether a user of the user device 610 is logged into the social networking system 630, which may enable modification of the data communicated from the social networking system 630 to the user device 610.

The external system 620 includes one or more web servers that include one or more web pages 622 a, 622 b, which are communicated to the user device 610 using the network 650. The external system 620 is separate from the social networking system 630. For example, the external system 620 is associated with a first domain, while the social networking system 630 is associated with a separate social networking domain. Web pages 622 a, 622 b, included in the external system 620, comprise markup language documents 614 identifying content and including instructions specifying formatting or presentation of the identified content.

The social networking system 630 includes one or more computing devices for a social network, including a plurality of users, and providing users of the social network with the ability to communicate and interact with other users of the social network. In some instances, the social network can be represented by a graph, i.e., a data structure including edges and nodes. Other data structures can also be used to represent the social network, including but not limited to databases, objects, classes, meta elements, files, or any other data structure. The social networking system 630 may be administered, managed, or controlled by an operator. The operator of the social networking system 630 may be a human being, an automated application, or a series of applications for managing content, regulating policies, and collecting usage metrics within the social networking system 630. Any type of operator may be used.

Users may join the social networking system 630 and then add connections to any number of other users of the social networking system 630 to whom they desire to be connected. As used herein, the term “friend” refers to any other user of the social networking system 630 to whom a user has formed a connection, association, or relationship via the social networking system 630. For example, in an embodiment, if users in the social networking system 630 are represented as nodes in the social graph, the term “friend” can refer to an edge formed between and directly connecting two user nodes.

Connections may be added explicitly by a user or may be automatically created by the social networking system 630 based on common characteristics of the users (e.g., users who are alumni of the same educational institution). For example, a first user specifically selects a particular other user to be a friend. Connections in the social networking system 630 are usually in both directions, but need not be, so the terms “user” and “friend” depend on the frame of reference. Connections between users of the social networking system 630 are usually bilateral (“two-way”), or “mutual,” but connections may also be unilateral, or “one-way.” For example, if Bob and Joe are both users of the social networking system 630 and connected to each other, Bob and Joe are each other's connections. If, on the other hand, Bob wishes to connect to Joe to view data communicated to the social networking system 630 by Joe, but Joe does not wish to form a mutual connection, a unilateral connection may be established. The connection between users may be a direct connection; however, some embodiments of the social networking system 630 allow the connection to be indirect via one or more levels of connections or degrees of separation.

In addition to establishing and maintaining connections between users and allowing interactions between users, the social networking system 630 provides users with the ability to take actions on various types of items supported by the social networking system 630. These items may include groups or networks (i.e., social networks of people, entities, and concepts) to which users of the social networking system 630 may belong, events or calendar entries in which a user might be interested, computer-based applications that a user may use via the social networking system 630, transactions that allow users to buy or sell items via services provided by or through the social networking system 630, and interactions with advertisements that a user may perform on or off the social networking system 630. These are just a few examples of the items upon which a user may act on the social networking system 630, and many others are possible. A user may interact with anything that is capable of being represented in the social networking system 630 or in the external system 620, separate from the social networking system 630, or coupled to the social networking system 630 via the network 650.

The social networking system 630 is also capable of linking a variety of entities. For example, the social networking system 630 enables users to interact with each other as well as external systems 620 or other entities through an API, a web service, or other communication channels. The social networking system 630 generates and maintains the “social graph” comprising a plurality of nodes interconnected by a plurality of edges. Each node in the social graph may represent an entity that can act on another node and/or that can be acted on by another node. The social graph may include various types of nodes. Examples of types of nodes include users, non-person entities, content items, web pages, groups, activities, messages, concepts, and any other things that can be represented by an object in the social networking system 630. An edge between two nodes in the social graph may represent a particular kind of connection, or association, between the two nodes, which may result from node relationships or from an action that was performed by one of the nodes on the other node. In some cases, the edges between nodes can be weighted. The weight of an edge can represent an attribute associated with the edge, such as a strength of the connection or association between nodes. Different types of edges can be provided with different weights. For example, an edge created when one user “likes” another user may be given one weight, while an edge created when a user befriends another user may be given a different weight.

As an example, when a first user identifies a second user as a friend, an edge in the social graph is generated connecting a node representing the first user and a second node representing the second user. As various nodes relate or interact with each other, the social networking system 630 modifies edges connecting the various nodes to reflect the relationships and interactions.

The social networking system 630 also includes user-generated content, which enhances a user's interactions with the social networking system 630. User-generated content may include anything a user can add, upload, send, or “post” to the social networking system 630. For example, a user communicates posts to the social networking system 630 from a user device 610. Posts may include data such as status updates or other textual data, location information, images such as photos, videos, links, music or other similar data and/or media. Content may also be added to the social networking system 630 by a third party. Content “items” are represented as objects in the social networking system 630. In this way, users of the social networking system 630 are encouraged to communicate with each other by posting text and content items of various types of media through various communication channels. Such communication increases the interaction of users with each other and increases the frequency with which users interact with the social networking system 630.

The social networking system 630 includes a web server 632, an API request server 634, a user profile store 636, a connection store 638, an action logger 640, an activity log 642, and an authorization server 644. In an embodiment of the invention, the social networking system 630 may include additional, fewer, or different components for various applications. Other components, such as network interfaces, security mechanisms, load balancers, failover servers, management and network operations consoles, and the like are not shown so as to not obscure the details of the system.

The user profile store 636 maintains information about user accounts, including biographic, demographic, and other types of descriptive information, such as work experience, educational history, hobbies or preferences, location, and the like that has been declared by users or inferred by the social networking system 630. This information is stored in the user profile store 636 such that each user is uniquely identified. The social networking system 630 also stores data describing one or more connections between different users in the connection store 638. The connection information may indicate users who have similar or common work experience, group memberships, hobbies, or educational history. Additionally, the social networking system 630 includes user-defined connections between different users, allowing users to specify their relationships with other users. For example, user-defined connections allow users to generate relationships with other users that parallel the users' real-life relationships, such as friends, co-workers, partners, and so forth. Users may select from predefined types of connections, or define their own connection types as needed. Connections with other nodes in the social networking system 630, such as non-person entities, buckets, cluster centers, images, interests, pages, external systems, concepts, and the like are also stored in the connection store 638.

The social networking system 630 maintains data about objects with which a user may interact. To maintain this data, the user profile store 636 and the connection store 638 store instances of the corresponding type of objects maintained by the social networking system 630. Each object type has information fields that are suitable for storing information appropriate to the type of object. For example, the user profile store 636 contains data structures with fields suitable for describing a user's account and information related to a user's account. When a new object of a particular type is created, the social networking system 630 initializes a new data structure of the corresponding type, assigns a unique object identifier to it, and begins to add data to the object as needed. This might occur, for example, when a user becomes a user of the social networking system 630, the social networking system 630 generates a new instance of a user profile in the user profile store 636, assigns a unique identifier to the user account, and begins to populate the fields of the user account with information provided by the user.

The connection store 638 includes data structures suitable for describing a user's connections to other users, connections to external systems 620 or connections to other entities. The connection store 638 may also associate a connection type with a user's connections, which may be used in conjunction with the user's privacy setting to regulate access to information about the user. In an embodiment of the invention, the user profile store 636 and the connection store 638 may be implemented as a federated database.

Data stored in the connection store 638, the user profile store 636, and the activity log 642 enables the social networking system 630 to generate the social graph that uses nodes to identify various objects and edges connecting nodes to identify relationships between different objects. For example, if a first user establishes a connection with a second user in the social networking system 630, user accounts of the first user and the second user from the user profile store 636 may act as nodes in the social graph. The connection between the first user and the second user stored by the connection store 638 is an edge between the nodes associated with the first user and the second user. Continuing this example, the second user may then send the first user a message within the social networking system 630. The action of sending the message, which may be stored, is another edge between the two nodes in the social graph representing the first user and the second user. Additionally, the message itself may be identified and included in the social graph as another node connected to the nodes representing the first user and the second user.

In another example, a first user may tag a second user in an image that is maintained by the social networking system 630 (or, alternatively, in an image maintained by another system outside of the social networking system 630). The image may itself be represented as a node in the social networking system 630. This tagging action may create edges between the first user and the second user as well as create an edge between each of the users and the image, which is also a node in the social graph. In yet another example, if a user confirms attending an event, the user and the event are nodes obtained from the user profile store 636, where the attendance of the event is an edge between the nodes that may be retrieved from the activity log 642. By generating and maintaining the social graph, the social networking system 630 includes data describing many different types of objects and the interactions and connections among those objects, providing a rich source of socially relevant information.

The web server 632 links the social networking system 630 to one or more user devices 610 and/or one or more external systems 620 via the network 650. The web server 632 serves web pages, as well as other web-related content, such as Java, JavaScript, Flash, XML, and so forth. The web server 632 may include a mail server or other messaging functionality for receiving and routing messages between the social networking system 630 and one or more user devices 610. The messages can be instant messages, queued messages (e.g., email), text and SMS messages, or any other suitable messaging format.

The API request server 634 allows one or more external systems 620 and user devices 610 to call access information from the social networking system 630 by calling one or more API functions. The API request server 634 may also allow external systems 620 to send information to the social networking system 630 by calling APIs. The external system 620, in one embodiment, sends an API request to the social networking system 630 via the network 650, and the API request server 634 receives the API request. The API request server 634 processes the request by calling an API associated with the API request to generate an appropriate response, which the API request server 634 communicates to the external system 620 via the network 650. For example, responsive to an API request, the API request server 634 collects data associated with a user, such as the user's connections that have logged into the external system 620, and communicates the collected data to the external system 620. In another embodiment, the user device 610 communicates with the social networking system 630 via APIs in the same manner as external systems 620.

The action logger 640 is capable of receiving communications from the web server 632 about user actions on and/or off the social networking system 630. The action logger 640 populates the activity log 642 with information about user actions, enabling the social networking system 630 to discover various actions taken by its users within the social networking system 630 and outside of the social networking system 630. Any action that a particular user takes with respect to another node on the social networking system 630 may be associated with each user's account, through information maintained in the activity log 642 or in a similar database or other data repository. Examples of actions taken by a user within the social networking system 630 that are identified and stored may include, for example, adding a connection to another user, sending a message to another user, reading a message from another user, viewing content associated with another user, attending an event posted by another user, posting an image, attempting to post an image, or other actions interacting with another user or another object. When a user takes an action within the social networking system 630, the action is recorded in the activity log 642. In one embodiment, the social networking system 630 maintains the activity log 642 as a database of entries. When an action is taken within the social networking system 630, an entry for the action is added to the activity log 642. The activity log 642 may be referred to as an action log.

Additionally, user actions may be associated with concepts and actions that occur within an entity outside of the social networking system 630, such as an external system 620 that is separate from the social networking system 630. For example, the action logger 640 may receive data describing a user's interaction with an external system 620 from the web server 632. In this example, the external system 620 reports a user's interaction according to structured actions and objects in the social graph.

Other examples of actions where a user interacts with an external system 620 include a user expressing an interest in an external system 620 or another entity, a user posting a comment to the social networking system 630 that discusses an external system 620 or a web page 622 a within the external system 620, a user posting to the social networking system 630 a Uniform Resource Locator (URL) or other identifier associated with an external system 620, a user attending an event associated with an external system 620, or any other action by a user that is related to an external system 620. Thus, the activity log 642 may include actions describing interactions between a user of the social networking system 630 and an external system 620 that is separate from the social networking system 630.

The authorization server 644 enforces one or more privacy settings of the users of the social networking system 630. A privacy setting of a user determines how particular information associated with a user can be shared. The privacy setting comprises the specification of particular information associated with a user and the specification of the entity or entities with whom the information can be shared. Examples of entities with which information can be shared may include other users, applications, external systems 620, or any entity that can potentially access the information. The information that can be shared by a user comprises user account information, such as profile photos, phone numbers associated with the user, user's connections, actions taken by the user such as adding a connection, changing user profile information, and the like.

The privacy setting specification may be provided at different levels of granularity. For example, the privacy setting may identify specific information to be shared with other users; the privacy setting identifies a work phone number or a specific set of related information, such as, personal information including profile photo, home phone number, and status. Alternatively, the privacy setting may apply to all the information associated with the user. The specification of the set of entities that can access particular information can also be specified at various levels of granularity. Various sets of entities with which information can be shared may include, for example, all friends of the user, all friends of friends, all applications, or all external systems 620. One embodiment allows the specification of the set of entities to comprise an enumeration of entities. For example, the user may provide a list of external systems 620 that are allowed to access certain information. Another embodiment allows the specification to comprise a set of entities along with exceptions that are not allowed to access the information. For example, a user may allow all external systems 620 to access the user's work information, but specify a list of external systems 620 that are not allowed to access the work information. Certain embodiments call the list of exceptions that are not allowed to access certain information a “block list”. External systems 620 belonging to a block list specified by a user are blocked from accessing the information specified in the privacy setting. Various combinations of granularity of specification of information, and granularity of specification of entities, with which information is shared are possible. For example, all personal information may be shared with friends whereas all work information may be shared with friends of friends.

The authorization server 644 contains logic to determine if certain information associated with a user can be accessed by a user's friends, external systems 620, and/or other applications and entities. The external system 620 may need authorization from the authorization server 644 to access the user's more private and sensitive information, such as the user's work phone number. Based on the user's privacy settings, the authorization server 644 determines if another user, the external system 620, an application, or another entity is allowed to access information associated with the user, including information about actions taken by the user.

In some embodiments, the social networking system 630 can include an user navigation pattern module 646. The user navigation pattern module 646 can be implemented with the user navigation pattern module 102, as discussed in more detail herein. In some embodiments, one or more functionalities of the user navigation pattern module 646 can be implemented in the user device 610.

Hardware Implementation

The foregoing processes and features can be implemented by a wide variety of machine and computer system architectures and in a wide variety of network and computing environments. FIG. 7 illustrates an example of a computer system 700 that may be used to implement one or more of the embodiments described herein in accordance with an embodiment of the invention. The computer system 700 includes sets of instructions for causing the computer system 700 to perform the processes and features discussed herein. The computer system 700 may be connected (e.g., networked) to other machines. In a networked deployment, the computer system 700 may operate in the capacity of a server machine or a client machine in a client-server network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. In an embodiment of the invention, the computer system 700 may be the social networking system 630, the user device 610, and the external system 720, or a component thereof. In an embodiment of the invention, the computer system 700 may be one server among many that constitutes all or part of the social networking system 630.

The computer system 700 includes a processor 702, a cache 704, and one or more executable modules and drivers, stored on a computer-readable medium, directed to the processes and features described herein. Additionally, the computer system 700 includes a high performance input/output (I/O) bus 706 and a standard I/O bus 708. A host bridge 710 couples processor 702 to high performance I/O bus 706, whereas I/O bus bridge 712 couples the two buses 706 and 708 to each other. A system memory 714 and one or more network interfaces 716 couple to high performance I/O bus 706. The computer system 700 may further include video memory and a display device coupled to the video memory (not shown). Mass storage 718 and I/O ports 720 couple to the standard I/O bus 708. The computer system 700 may optionally include a keyboard and pointing device, a display device, or other input/output devices (not shown) coupled to the standard I/O bus 708. Collectively, these elements are intended to represent a broad category of computer hardware systems, including but not limited to computer systems based on the x86-compatible processors manufactured by Intel Corporation of Santa Clara, Calif., and the x86-compatible processors manufactured by Advanced Micro Devices (AMD), Inc., of Sunnyvale, Calif., as well as any other suitable processor.

An operating system manages and controls the operation of the computer system 700, including the input and output of data to and from software applications (not shown). The operating system provides an interface between the software applications being executed on the system and the hardware components of the system. Any suitable operating system may be used, such as the LINUX Operating System, the Apple Macintosh Operating System, available from Apple Computer Inc. of Cupertino, Calif., UNIX operating systems, Microsoft® Windows® operating systems, BSD operating systems, and the like. Other implementations are possible.

The elements of the computer system 700 are described in greater detail below. In particular, the network interface 716 provides communication between the computer system 700 and any of a wide range of networks, such as an Ethernet (e.g., IEEE 802.3) network, a backplane, etc. The mass storage 718 provides permanent storage for the data and programming instructions to perform the above-described processes and features implemented by the respective computing systems identified above, whereas the system memory 714 (e.g., DRAM) provides temporary storage for the data and programming instructions when executed by the processor 702. The I/O ports 720 may be one or more serial and/or parallel communication ports that provide communication between additional peripheral devices, which may be coupled to the computer system 700.

The computer system 700 may include a variety of system architectures, and various components of the computer system 700 may be rearranged. For example, the cache 704 may be on-chip with processor 702. Alternatively, the cache 704 and the processor 702 may be packed together as a “processor module”, with processor 702 being referred to as the “processor core”. Furthermore, certain embodiments of the invention may neither require nor include all of the above components. For example, peripheral devices coupled to the standard I/O bus 708 may couple to the high performance I/O bus 706. In addition, in some embodiments, only a single bus may exist, with the components of the computer system 700 being coupled to the single bus. Moreover, the computer system 700 may include additional components, such as additional processors, storage devices, or memories.

In general, the processes and features described herein may be implemented as part of an operating system or a specific application, component, program, object, module, or series of instructions referred to as “programs”. For example, one or more programs may be used to execute specific processes described herein. The programs typically comprise one or more instructions in various memory and storage devices in the computer system 700 that, when read and executed by one or more processors, cause the computer system 700 to perform operations to execute the processes and features described herein. The processes and features described herein may be implemented in software, firmware, hardware (e.g., an application specific integrated circuit), or any combination thereof.

In one implementation, the processes and features described herein are implemented as a series of executable modules run by the computer system 700, individually or collectively in a distributed computing environment. The foregoing modules may be realized by hardware, executable modules stored on a computer-readable medium (or machine-readable medium), or a combination of both. For example, the modules may comprise a plurality or series of instructions to be executed by a processor in a hardware system, such as the processor 702. Initially, the series of instructions may be stored on a storage device, such as the mass storage 718. However, the series of instructions can be stored on any suitable computer readable storage medium. Furthermore, the series of instructions need not be stored locally, and could be received from a remote storage device, such as a server on a network, via the network interface 716. The instructions are copied from the storage device, such as the mass storage 718, into the system memory 714 and then accessed and executed by the processor 702. In various implementations, a module or modules can be executed by a processor or multiple processors in one or multiple locations, such as multiple servers in a parallel processing environment.

Examples of computer-readable media include, but are not limited to, recordable type media such as volatile and non-volatile memory devices; solid state memories; floppy and other removable disks; hard disk drives; magnetic media; optical disks (e.g., Compact Disk Read-Only Memory (CD ROMS), Digital Versatile Disks (DVDs)); other similar non-transitory (or transitory), tangible (or non-tangible) storage medium; or any type of medium suitable for storing, encoding, or carrying a series of instructions for execution by the computer system 700 to perform any one or more of the processes and features described herein.

For purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the description. It will be apparent, however, to one skilled in the art that embodiments of the disclosure can be practiced without these specific details. In some instances, modules, structures, processes, features, and devices are shown in block diagram form in order to avoid obscuring the description. In other instances, functional block diagrams and flow diagrams are shown to represent data and logic flows. The components of block diagrams and flow diagrams (e.g., modules, blocks, structures, devices, features, etc.) may be variously combined, separated, removed, reordered, and replaced in a manner other than as expressly described and depicted herein.

Reference in this specification to “one embodiment”, “an embodiment”, “other embodiments”, “one series of embodiments”, “some embodiments”, “various embodiments”, or the like means that a particular feature, design, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the disclosure. The appearances of, for example, the phrase “in one embodiment” or “in an embodiment” in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Moreover, whether or not there is express reference to an “embodiment” or the like, various features are described, which may be variously combined and included in some embodiments, but also variously omitted in other embodiments. Similarly, various features are described that may be preferences or requirements for some embodiments, but not other embodiments.

The language used herein has been principally selected for readability and instructional purposes, and it may not have been selected to delineate or circumscribe the inventive subject matter. It is therefore intended that the scope of the invention be limited not by this detailed description, but rather by any claims that issue on an application based hereon. Accordingly, the disclosure of the embodiments of the invention is intended to be illustrative, but not limiting, of the scope of the invention, which is set forth in the following claims. 

1. A computer-implemented method comprising: monitoring, by a computing system, transitions of users between pages of a system; maintaining, by the computing system, a table of the transitions of the users, wherein the table includes a user ID, an origin page, a destination page, a number of counts for a transition from the origin page to the destination page, and a frequency of the transition; and determining, by the computing system, user navigation data based on the table.
 2. The method of claim 1, further comprising: determining, by the computing system, frequency vectors for the users based on the user navigation data, wherein the frequency vectors include frequencies for all types of transitions of the users.
 3. The method of claim 2, further comprising: generating, by the computing system, a frequency vector matrix based on the frequency vectors for the users; and generating, by the computing system, at least one of: a reduced user vector matrix, an eigenvalue matrix, or an eigenvector matrix based on singular value decomposition of the frequency vector matrix.
 4. The method of claim 3, further comprising: determining, by the computing system, a cluster of the users based on the reduced user vector matrix, wherein the cluster of the users corresponds with a particular user navigation pattern.
 5. The method of claim 4, further comprising: determining, by the computing system, the particular user navigation pattern based on a centroid of the cluster multiplied by eigenvectors associated with the eigenvector matrix and eigenvalues associated with the eigenvalue matrix.
 6. The method of claim 3, further comprising: determining, by the computing system, a number of clusters based on a number of possible types of transitions.
 7. The method of claim 1, further comprising: normalizing, by the computing system, numbers of counts for the transitions of the users such that a sum of normalized transition counts for a user is one.
 8. The method of claim 1, wherein the frequency of the transition is determined based on a fraction of a count of a type of transition from the origin page to the destination page over a count of all types of transitions.
 9. The method of claim 1, wherein the pages of the system include an off application state associated with an off state of an application on which the pages are provided.
 10. The method of claim 1, wherein the transitions of users are based on a selection of a menu item or a performance of a touch gesture
 11. A computing system comprising: at least one processor; and a memory storing instructions that, when executed by the at least one processor, cause the computing system to perform: monitoring transitions of users between pages of a system; maintaining a table of the transitions of the users, wherein the table includes a user ID, an origin page, a destination page, a number of counts for a transition from the origin page to the destination page, and a frequency of the transition; and determining user navigation data based on the table.
 12. The computing system of claim 11, wherein the instructions further cause the computing system to perform: determining, by the computing system, frequency vectors for the users based on the user navigation data, wherein the frequency vectors include frequencies for all types of transitions of the users.
 13. The computing system of claim 12, wherein the instructions further cause the computing system to perform: generating, by the computing system, a frequency vector matrix that includes the frequency vectors for the users; and generating, by the computing system, at least one of: a reduced user vector matrix, an eigenvalue matrix, or an eigenvector matrix based on singular value decomposition of the frequency vector matrix.
 14. The computing system of claim 13, wherein the instructions further cause the computing system to perform: determining, by the computing system, a cluster of the users based on the reduced user vector matrix, wherein the cluster of the users corresponds with a particular user navigation pattern.
 15. The computing system of claim 14, wherein the instructions further cause the computing system to perform: determining, by the computing system, the particular user navigation pattern based on a centroid of the cluster multiplied by eigenvectors associated with the eigenvector matrix and eigenvalues associated with the eigenvalue matrix.
 16. A non-transitory computer-readable storage medium including instructions that, when executed by at least on processor of a computing system, cause the computing system to perform: monitoring transitions of users between pages of a system; maintaining a table of the transitions of the users, wherein the table includes a user ID, an origin page, a destination page, a number of counts for a transition from the origin page to the destination page, and a frequency of the transition; and determining user navigation data based on the table.
 17. The non-transitory computer-readable storage medium of claim 16, wherein the instructions further cause the computing system to perform: determining, by the computing system, frequency vectors for the users based on the user navigation data, wherein the frequency vectors include frequencies for all types of transitions of the users.
 18. The non-transitory computer-readable storage medium of claim 17, wherein the instructions further cause the computing system to perform: generating, by the computing system, a frequency vector matrix that includes the frequency vectors for the users; and generating, by the computing system, at least one of: a reduced user vector matrix, an eigenvalue matrix, or an eigenvector matrix based on singular value decomposition of the frequency vector matrix.
 19. The non-transitory computer-readable storage medium of claim 18, wherein the instructions further cause the computing system to perform: determining, by the computing system, a cluster of the users based on the reduced user vector matrix, wherein the cluster of the users corresponds with a particular user navigation pattern.
 20. The non-transitory computer-readable storage medium of claim 19, wherein the instructions further cause the computing system to perform: determining, by the computing system, the particular user navigation pattern based on a centroid of the cluster multiplied by eigenvectors associated with the eigenvector matrix and eigenvalues associated with the eigenvalue matrix. 